


import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_=[]							):



     """simple docstring"""






     snake_case											=							size[0] - overlap_pixels * 2
     snake_case											=							size[1] - overlap_pixels * 2
     for letter in ["l", "r"]:
          if letter in remove_borders:
               size_x += overlap_pixels
     for letter in ["t", "b"]:
          if letter in remove_borders:
               size_y += overlap_pixels
     snake_case											=							np.ones((size_y, size_x)				,dtype=np.uinta							) * 2_55
     snake_case											=							np.pad(UpperCamelCase_				,mode='''linear_ramp'''				,pad_width=UpperCamelCase_				,end_values=0							)

     if "l" in remove_borders:
          snake_case											=							mask[:, overlap_pixels : mask.shape[1]]
     if "r" in remove_borders:
          snake_case											=							mask[:, 0 : mask.shape[1] - overlap_pixels]
     if "t" in remove_borders:
          snake_case											=							mask[overlap_pixels : mask.shape[0], :]
     if "b" in remove_borders:
          snake_case											=							mask[0 : mask.shape[0] - overlap_pixels, :]
     return mask
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     return max(UpperCamelCase_				,min(UpperCamelCase_				,UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     return (
         clamp(rect[0]				,min[0]				,max[0]							),
         clamp(rect[1]				,min[1]				,max[1]							),
         clamp(rect[2]				,min[0]				,max[0]							),
         clamp(rect[3]				,min[1]				,max[1]							),
     )
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							list(UpperCamelCase_							)
     rect[0] -= overlap
     rect[1] -= overlap
     rect[2] += overlap
     rect[3] += overlap
     snake_case											=							clamp_rect(UpperCamelCase_				,[0, 0]				,[image_size[0], image_size[1]]							)
     return rect
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							Image.new('''RGB'''				,(tile.size[0] + original_slice, tile.size[1])							)
     result.paste(
         original_image.resize((tile.size[0], tile.size[1])				,Image.BICUBIC							).crop(
             (slice_x, 0, slice_x + original_slice, tile.size[1])							)				,(0, 0)				,)
     result.paste(UpperCamelCase_				,(original_slice, 0)							)
     return result
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							(original_image_slice * 4, 0, tile.size[0], tile.size[1])
     snake_case											=							tile.crop(UpperCamelCase_							)
     return tile
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							n % d
     return n - divisor

class     A__   (       snake_case__      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case = 3_5_0     ,   ):
         super().__init__(
             vae=__snake_case     ,   text_encoder=__snake_case     ,   tokenizer=__snake_case     ,   unet=__snake_case     ,   low_res_scheduler=__snake_case     ,   scheduler=__snake_case     ,   max_noise_level=__snake_case     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   **__snake_case       ):
         torch.manual_seed(0       )
         snake_case											=							(
             min(image.size[0] - (tile_size + original_image_slice)     ,   x * tile_size       ),
             min(image.size[1] - (tile_size + original_image_slice)     ,   y * tile_size       ),
             min(image.size[0]     ,   (x + 1) * tile_size       ),
             min(image.size[1]     ,   (y + 1) * tile_size       ),
         )
         snake_case											=							add_overlap_rect(__snake_case     ,   __snake_case     ,   image.size       )
         snake_case											=							image.crop(__snake_case       )
         snake_case											=							((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
         snake_case											=							translated_slice_x - (original_image_slice / 2)
         snake_case											=							max(0     ,   __snake_case       )
         snake_case											=							squeeze_tile(__snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       )
         snake_case											=							to_input.size
         snake_case											=							to_input.resize((tile_size, tile_size)     ,   Image.BICUBIC       )
         snake_case											=							super(__snake_case     ,   self       ).__call__(image=__snake_case     ,   **__snake_case       ).images[0]
         snake_case											=							upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)     ,   Image.BICUBIC       )
         snake_case											=							unsqueeze_tile(__snake_case     ,   __snake_case       )
         snake_case											=							upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)     ,   Image.BICUBIC       )
         snake_case											=							[]
         if x == 0:
              remove_borders.append('''l'''       )
         elif crop_rect[2] == image.size[0]:
              remove_borders.append('''r'''       )
         if y == 0:
              remove_borders.append('''t'''       )
         elif crop_rect[3] == image.size[1]:
              remove_borders.append('''b'''       )
         snake_case											=							Image.fromarray(
             make_transparency_mask(
                 (upscaled_tile.size[0], upscaled_tile.size[1])     ,   tile_border * 4     ,   remove_borders=__snake_case       )     ,   mode='''L'''     ,   )
         final_image.paste(
             __snake_case     ,   (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)     ,   __snake_case       )




    @torch.no_grad()
    def __call__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case = 7_5     ,   __snake_case = 9.0     ,   __snake_case = 5_0     ,   __snake_case = None     ,   __snake_case = 1     ,   __snake_case = 0.0     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = 1     ,   __snake_case = 1_2_8     ,   __snake_case = 3_2     ,   __snake_case = 3_2     ,   ):
         snake_case											=							Image.new('''RGB'''     ,   (image.size[0] * 4, image.size[1] * 4)       )
         snake_case											=							math.ceil(image.size[0] / tile_size       )
         snake_case											=							math.ceil(image.size[1] / tile_size       )
         snake_case											=							tcx * tcy
         snake_case											=							0
         for y in range(__snake_case       ):
              for x in range(__snake_case       ):
                   self._process_tile(
                       __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   prompt=__snake_case     ,   num_inference_steps=__snake_case     ,   guidance_scale=__snake_case     ,   noise_level=__snake_case     ,   negative_prompt=__snake_case     ,   num_images_per_prompt=__snake_case     ,   eta=__snake_case     ,   generator=__snake_case     ,   latents=__snake_case     ,   )
                   current_count += 1
                   if callback is not None:
                        callback({'''progress''': current_count / total_tile_count, '''image''': final_image}       )
         return final_image






def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							'''stabilityai/stable-diffusion-x4-upscaler'''
     snake_case											=							StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase_				,revision='''fp16'''				,torch_dtype=torch.floataa							)
     snake_case											=							pipe.to('''cuda'''							)
     snake_case											=							Image.open('''../../docs/source/imgs/diffusers_library.jpg'''							)

     def callback(UpperCamelCase_							):
          print(F'''progress: {obj["progress"]:.4f}'''							)
          obj["image"].save('''diffusers_library_progress.jpg'''							)

     snake_case											=							pipe(image=UpperCamelCase_				,prompt='''Black font, white background, vector'''				,noise_level=40				,callback=UpperCamelCase_							)
     final_image.save('''diffusers_library.jpg'''							)


if __name__ == "__main__":
 main()








import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE     :					List[str]      =       get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE     :					Any      =       25_00_04
_SCREAMING_SNAKE_CASE     :					Any      =       25_00_20

@require_sentencepiece
@require_tokenizers
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						MBartaaTokenizer
    __magic_name__         =						MBartaaTokenizerFast
    __magic_name__         =						True
    __magic_name__         =						True
    def 						a_     (			self       ):
         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )
         tokenizer.save_pretrained(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							'''<s>'''
         snake_case											=							0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case       )     ,   __snake_case       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0]     ,   '''<s>'''       )
         self.assertEqual(vocab_keys[1]     ,   '''<pad>'''       )
         self.assertEqual(vocab_keys[-1]     ,   '''<mask>'''       )
         self.assertEqual(len(__snake_case       )     ,   1_0_5_4       )
    def 						a_     (			self       ):
         self.assertEqual(self.get_tokenizer().vocab_size     ,   1_0_5_4       )
    def 						a_     (			self       ):
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )

         snake_case											=							tokenizer.tokenize('''This is a test'''       )
         self.assertListEqual(__snake_case     ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__snake_case       )     ,   [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,   )

         snake_case											=							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']     ,   )
         snake_case											=							tokenizer.convert_tokens_to_ids(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             ]     ,   )

         snake_case											=							tokenizer.convert_ids_to_tokens(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']     ,   )
    @slow
    def 						a_     (			self       ):
         # fmt: off
         snake_case											=							{'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__snake_case     ,   model_name='''facebook/mbart-large-50'''     ,   revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''     ,   )




    def 						a_     (			self       ):
         if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

         snake_case											=							(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   snake_case											=							self.rust_tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )
                   snake_case											=							self.tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )

                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )
                   snake_case											=							tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f       )
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )
                        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=True
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=False
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )






@require_torch
@require_sentencepiece
@require_tokenizers
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						'facebook/mbart-large-50-one-to-many-mmt'
    __magic_name__         =						[
        ' UN Chief Says There Is No Military Solution in Syria',
        ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
    ]
    __magic_name__         =						[
        'Şeful ONU declară că nu există o soluţie militară în Siria',
        'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
        ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
        ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
    ]
    __magic_name__         =						[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]
    @classmethod
    def 						a_     (			cls       ):
         snake_case											=							MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''       )
         snake_case											=							1
         return cls
    def 						a_     (			self       ):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']     ,   2_5_0_0_0_1       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']     ,   2_5_0_0_0_4       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']     ,   2_5_0_0_2_0       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']     ,   2_5_0_0_3_8       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertIn(__snake_case     ,   self.tokenizer.all_special_ids       )
         snake_case											=							[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
         snake_case											=							self.tokenizer.decode(__snake_case     ,   skip_special_tokens=__snake_case       )
         snake_case											=							self.tokenizer.decode(generated_ids[1:]     ,   skip_special_tokens=__snake_case       )
         self.assertEqual(__snake_case     ,   __snake_case       )
         self.assertNotIn(self.tokenizer.eos_token     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							['''this is gunna be a long sentence ''' * 2_0]
         assert isinstance(src_text[0]     ,   __snake_case       )
         snake_case											=							1_0
         snake_case											=							self.tokenizer(__snake_case     ,   max_length=__snake_case     ,   truncation=__snake_case       ).input_ids[0]
         self.assertEqual(ids[0]     ,   __snake_case       )
         self.assertEqual(ids[-1]     ,   2       )
         self.assertEqual(len(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']       )     ,   [2_5_0_0_5_3, 2_5_0_0_0_1]       )
    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()
         snake_case											=							self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__snake_case       )
         snake_case											=							MBartaaTokenizer.from_pretrained(__snake_case       )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,   __snake_case       )
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   return_tensors='''pt'''       )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(
             self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=len(self.expected_src_tokens       )     ,   return_tensors='''pt'''     ,   )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         self.assertIsInstance(__snake_case     ,   __snake_case       )

         self.assertEqual((2, 1_4)     ,   batch.input_ids.shape       )
         self.assertEqual((2, 1_4)     ,   batch.attention_mask.shape       )
         snake_case											=							batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
         self.assertEqual(2     ,   batch.decoder_input_ids[0, 0]       )  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens     ,   [EN_CODE]       )
         self.assertEqual(self.tokenizer.suffix_tokens     ,   [self.tokenizer.eos_token_id]       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=3     ,   return_tensors='''pt'''       )
         snake_case											=							self.tokenizer(
             text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=1_0     ,   return_tensors='''pt'''       )
         snake_case											=							targets['''input_ids''']
         snake_case											=							shift_tokens_right(__snake_case     ,   self.tokenizer.pad_token_id       )

         self.assertEqual(batch.input_ids.shape[1]     ,   3       )
         self.assertEqual(batch.decoder_input_ids.shape[1]     ,   1_0       )




    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer._build_translation_inputs(
             '''A test'''     ,   return_tensors='''pt'''     ,   src_lang='''en_XX'''     ,   tgt_lang='''ar_AR'''       )

         self.assertEqual(
             nested_simplify(__snake_case       )     ,   {
                 # en_XX, A, test, EOS
                 '''input_ids''': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
                 '''attention_mask''': [[1, 1, 1, 1]],
                 # ar_AR
                 '''forced_bos_token_id''': 2_5_0_0_0_1,
             }     ,   )




